Towards a Formal Framework for Mobile, Service-Oriented Sensor-Actuator Networks

Service-oriented sensor-actuator networks (SOSANETs) are deployed in health-critical applications like patient monitoring and have to fulfill strong safety requirements. However, a framework for the rigorous formal modeling and analysis of SOSANETs does not exist. In particular, there is currently no support for the verification of correct network behavior after node failure or loss/addition of communication links. To overcome this problem, we propose a formal framework for SOSANETs. The main idea is to base our framework on the \pi-calculus, a formally defined, compositional and well-established formalism. We choose KLAIM, an existing formal language based on the \pi-calculus as the foundation for our framework. With that, we are able to formally model SOSANETs with possible topology changes and network failures. This provides the basis for our future work on prediction, analysis and verification of the network behavior of these systems. Furthermore, we illustrate the real-life applicability of this approach by modeling and extending a use case scenario from the medical domain.Comment: In Proceedings FESCA 2013, arXiv:1302.478

Ein Framework für zuverlässige und dynamische Sensor-Aktor-Netzwerke

The number of persons requiring medical assistance in industrial nations grows with the demographic change. Unfortunately, the growth is unproportional to the availability of well-trained care personal. Wireless sensor-actuator networks have the potential to support care personal. Nodes worn by patients may supervise individually critical vital parameters and trigger an alarm if a critical value is reached. Medical help can be provided earlier lowering the risk of permanent health issues. Nevertheless, wireless sensor-actuator networks are error-prone. They have to be designed fault-tolerant to function reliably. However, an approach for provably reliable wireless networks is still non-existing. This thesis proposes a solution for this problem by providing a framework for the generation, supervision and maintenance of fault-tolerant wireless sensor actuator networks. A network is defined as fault-tolerant and reliable, if it is biconnected. At least two node-disjoint paths between every pair of nodes have to exist within the network. The main contributions of this thesis are threefold. First, an algorithm for the generation of fault-tolerant networks for given floor plans is provided. The generated topologies are biconnected and require only a reasonable number of nodes. Furthermore, they provide the infrastructure for a localization of nodes by covering each position within the floor plan with three signals. Second, a heuristic and distributed algorithm for the detection of bottlenecks in dynamic networks is introduced. Bottlenecks are possible breakpoints of the network and have to be discovered early to avoid a disconnection of nodes. The core is the assumption that every network is representable in the form of a graph. Graph theoretic measures are applied to detect topological changes. The two characteristics used for the heuristic are the algebraic connectivity and the Fiedler vector, both reflect the connectivity of the network. Time variations indicate critical topological changes in dynamic networks. A decentralized continuous algorithm is proposed, which estimates both characteristics utilizing the properties of a propagating discrete wave. The algorithm requires only local information avoiding a single-point of failure in the form of a central node. Third, an algorithm for the analysis and correction of faulty topologies is introduced. The topology of a network is analyzed using the well-known Ford-Fulkerson algorithm. The Ford-Fulkerson algorithm determines the maximal flow within a flow network. The maximal flow equals the number of edge-disjoint paths within a network with an edge capacity of one. Through a slight modification of the network, node-disjoint paths are found. If the number of node-disjoint paths is less than two, a bottleneck exists within the network. The bottleneck node is then identified and the network corrected through the placement of correction nodes. Finally, the placements are examined to avoid redundant correction nodes. The correct function of the algorithms is validated using a case study of a retirement home. All algorithms work without human interaction. Their application facilitates and accelerates the design, supervision and maintenance of wireless networks. The described framework provides a basis for the reliable application of sensor-actuator network in health care facilities.Der demographische Wandel in den Industrienationen führt zu einem Anstieg der Anzahl pflegebedürftiger Personen. Damit einhergehend steigt der Bedarf nach qualifiziertem Pflegepersonal. Dieser ist schwierig abzudecken. Gesundheitsassistenzsysteme in Form von drahtlosen Sensor-Aktornetzwerken könnten das Pflegepersonal in der täglichen Routine unterstützen. Patienten könnten mit Sensoren zur Beobachtung wesentlicher Vitalparameter ausgestattet werden, die bei Erreichen eines kritischen Wertes einen Alarm auslösen. Drahtlose Netzwerke sind im Allgemeinen fehleranfällig, müssen aber angewandt im Gesundheitswesen zuverlässig funktionieren. Knoten und Verbindungen können ausfallen. Bisher gibt es noch keinen Ansatz für den Entwurf und Betrieb von nachweislich zuverlässigen drahtlosen Netzwerken. Diese Arbeit präsentiert ein Framework zur Generierung, Überwachung und Wartung zuverlässiger drahtloser Sensor-Aktornetzwerke. Im Fokus steht dabei die Topologie des Netzwerkes. Die Zuverlässigkeit eines Netzwerkes wird über seine Konnektivität definiert: jedes Netzwerk muss fehlertolerant sein und im Notfall zwei voneinander unabhängige Kommunikationspfade für Alarmnachrichten bereitstellen, falls ein Pfad korrumpiert ist. Somit muss jedes Netzwerk zweifach zusammenhängend sein. Die wichtigsten Beiträge dieser Arbeit sind: ein Algorithmus zur Generierung fehlertoleranter Netzwerke für einen gegebenen Grundriss, eine Heuristik sowie ein Algorithmus zur Erkennung von Kommunikationsengpässen in dynamischen Netzwerken und ein Algorithmus zur Analyse und Korrektur fehlerhafter Netzwerktopologien. Der Generator erzeugt zweifach zusammenhängende Netzwerktopologien mit Signalabdeckungsraten, die eine Lokalisation von Knoten im Netzwerk durch Triangulierung erlauben. Die Erkennung von Kommunikationsengpässen beruht auf der Annahme, dass sich jedes Netzwerk als Graph darstellen lässt. Topologieänderungen werden durch die Evaluierung zeitlicher Änderungen zweier Konnektivitätsmaße, der algebraische Konnektivität und des Fiedler-Vektors, erkannt. Der Algorithmus zur Kommunikationsengpassdetektion ermöglicht eine dezentrale Selbstüberwachung des Netzwerkes ohne externe Intervention. Zur Berechnung der Konnektivitätsmaße werden nur lokal auf einem Knoten vorhandene Informationen benötigt. Die Korrekturroutine nutzt den bekannten Ford-Fulkerson Algorithmus zur Berechnung des maximalen Flusses innerhalb des Netzwerkes. Bei einer Kantenkapazität von eins und durch eine Modifikation des Netzwerkes entspricht dieser der Anzahl der Knoten-disjunkten Pfade im Netzwerk. Wenn nur ein Pfad gefunden wird, existiert ein Kommunikationsengpass. Dieser wird lokalisiert und mithilfe eines neuplatzierten Korrekturknotens behoben. Die Platzierung redundanter Knoten wird durch einen zusätzlichen Optimierungsschritt vermieden. Die Anwendbarkeit des Ansatzes wird durch eine ausführliche Evaluierung gezeigt. Alle Algorithmen wurden dazu implementiert bzw. im Falle des verteilten Netzwerkalgorithmus simuliert und anschließend anhand von unterschiedlichen Fallbeispielen getestet. Das präsentierte Framework ermöglicht und vereinfacht den Entwurf, die autonome Überwachung und die Wartung fehlertoleranter drahtloser Sensor-Aktornetzwerke

Design and Verification of a Health-Monitoring Driver Assistance System

Health-monitoring driver assistance systems support an independent and self-determined lifestyle enhancing the driver's safety. These systems are health-critical and need to guarantee correct behavior in emergency situations such as heart attacks. Furthermore, they have to be adjustable and extendable with respect to integrated functionalities to fit individual and changing needs. We present a concept for a mobile, service-oriented driver assistance system with dynamic network behavior. Additionally, we introduce a verification approach to ensure correct behavior

Detection of Periodontal Bone Loss on Periapical Radiographs—A Diagnostic Study Using Different Convolutional Neural Networks

Interest in machine learning models and convolutional neural networks (CNNs) for diagnostic purposes is steadily increasing in dentistry. Here, CNNs can potentially help in the classification of periodontal bone loss (PBL). In this study, the diagnostic performance of five CNNs in detecting PBL on periapical radiographs was analyzed. A set of anonymized periapical radiographs (N = 21,819) was evaluated by a group of trained and calibrated dentists and classified into radiographs without PBL or with mild, moderate, or severe PBL. Five CNNs were trained over five epochs. Statistically, diagnostic performance was analyzed using accuracy (ACC), sensitivity (SE), specificity (SP), and area under the receiver operating curve (AUC). Here, overall ACC ranged from 82.0% to 84.8%, SE 88.8–90.7%, SP 66.2–71.2%, and AUC 0.884–0.913, indicating similar diagnostic performance of the five CNNs. Furthermore, performance differences were evident in the individual sextant groups. Here, the highest values were found for the mandibular anterior teeth (ACC 94.9–96.0%) and the lowest values for the maxillary posterior teeth (78.0–80.7%). It can be concluded that automatic assessment of PBL seems to be possible, but that diagnostic accuracy varies depending on the location in the dentition. Future research is needed to improve performance for all tooth groups

Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks

Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to evaluate various models for automatized PBL detection. An image data set of 21,819 anonymized periapical radiographs from the upper/lower and anterior/posterior regions was assessed by calibrated dentists according to PBL. Five vision transformer networks (ViT-base/ViT-large from Google, BEiT-base/BEiT-large from Microsoft, DeiT-base from Facebook/Meta) were utilized and evaluated. Accuracy (ACC), sensitivity (SE), specificity (SP), positive/negative predictive value (PPV/NPV) and area under the ROC curve (AUC) were statistically determined. The overall diagnostic ACC and AUC values ranged from 83.4 to 85.2% and 0.899 to 0.918 for all evaluated transformer networks, respectively. Differences in diagnostic performance were evident for lower (ACC 94.1–96.7%; AUC 0.944–0.970) and upper anterior (86.7–90.2%; 0.948–0.958) and lower (85.6–87.2%; 0.913–0.937) and upper posterior teeth (78.1–81.0%; 0.851–0.875). In this study, only minor differences among the tested networks were detected for PBL detection. To increase the diagnostic performance and to support the clinical use of such networks, further optimisations with larger and manually annotated image data sets are needed